--- a
+++ b/layer.py
@@ -0,0 +1,44 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Time    : 2021/8/8 16:19
+# @Author  : Li Xiao
+# @File    : layer.py
+import torch
+import math
+from torch import nn
+from torch.nn.parameter import Parameter
+
+class GraphConvolution(nn.Module):
+    def __init__(self, infeas, outfeas, bias=True):
+        super(GraphConvolution,self).__init__()
+        self.in_features = infeas
+        self.out_features = outfeas
+        self.weight = Parameter(torch.FloatTensor(infeas, outfeas))
+        if bias:
+            self.bias = Parameter(torch.FloatTensor(outfeas))
+        else:
+            self.register_parameter('bias', None)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+
+        stdv = 1. / math.sqrt(self.weight.size(1))
+        self.weight.data.uniform_(-stdv, stdv)
+        if self.bias is not None:
+            self.bias.data.uniform_(-stdv,stdv)
+        '''
+        for name, param in GraphConvolution.named_parameters(self):
+            if 'weight' in name:
+                #torch.nn.init.constant_(param, val=0.1)
+                torch.nn.init.normal_(param, mean=0, std=0.1)
+            if 'bias' in name:
+                torch.nn.init.constant_(param, val=0)
+        '''
+
+    def forward(self, x, adj):
+        x1 = torch.mm(x, self.weight)
+        output = torch.mm(adj, x1)
+        if self.bias is not None:
+            return output + self.bias
+        else:
+            return output
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